System and method for image processing

ABSTRACT

The disclosure relates to a system and method for correcting PET image data. PET image data of a first part of a subject may be obtained. CT image data of a second part of the subject may be obtained. The first part may include the second part. PET voxel data of the first part may be obtained based on the PET image data of the first part. A relationship between the CT image data and PET voxel data of the second part may be determined. CT image data of a third part of the subject may be determined based on the relationship and PET voxel data of the third part. The first part may include the third part. An attenuation map may be determined based on the CT image data of the second part and the third part. The PET image data of the first part may be corrected based on the attenuation map.

CROSS-REFERENCE TO RELATED APPLICATIONS

This present application is a Continuation of U.S. patent applicationSer. No. 15/952,187 filed on Apr. 12, 2018, which claims priority toChinese Patent Application No. 201710277460.2 filed on Apr. 25, 2017,the entire content of each of which is hereby incorporated by reference.

TECHNICAL FIELD

This present disclosure generally relates to image processing, and moreparticularly, relates to a system and method for attenuation correctionin image reconstruction.

BACKGROUND

A positron emission tomography/computed tomography (PET/CT) device is acombination of a PET scanner and a CT scanner. In PET imaging, aradioactive tracer isotope may be injected into a subject to be scanned,and then annihilation events induced by the tracer isotope in thesubject may be detected by one or more detectors. One or more tomographyimages may be obtained based on the annihilation events. The CT scannermay be configured to obtain accurate distribution of the radioactivetracer isotope in the subject. Therefore, the PET/CT device has theadvantages of both the PET scanner and the CT scanner.

In PET imaging, a quantitative reconstruction of a tracer distributionrequires attenuation correction. An attenuation map may be needed forattenuation correction. The attenuation map may be typically acquiredthrough a transmission scan using the CT scanner, and then a correctedPET image may be determined based on the attenuation map. In somesituations, if a whole body of the subject is scanned using the CTscanner, a larger dose of radiation may be introduced to the subject, incomparison with that introduced to the subject when a partial body ofthe subject is scanned. Therefore, it would be desirable to provideeffective mechanisms for attenuation correction with reduced radiationdoses in CT scanning.

SUMMARY

One aspect of the present disclosure is directed to a method forcorrecting PET image data. The method may include one or more of thefollowing operations. PET image data of a first part of a subject may beobtained. CT image data of a second part of the subject may be obtained.The first part may include the second part. PET voxel data of the firstpart may be obtained based on the PET image data of the first part. Arelationship between the CT image data and PET voxel data of the secondpart may be determined. CT image data of a third part of the subject maybe determined based on the relationship and PET voxel data of the thirdpart. The first part may include the third part. An attenuation map maybe determined based on the CT image data of the second part and thethird part. The PET image data of the first part may be corrected basedon the attenuation map.

Another aspect of the present disclosure is directed to a systemincluding at least one storage device and at least one processor. The atleast one storage device may include a set of instructions or programs.The at least one processor may be configured to communicate with the atleast one storage device. When executing the set of instructions orprograms, the at least one processor may be configured to cause thesystem to perform one or more of the following operations. PET imagedata of a first part of a subject may be obtained. CT image data of asecond part of the subject may be obtained. The first part may includethe second part. PET voxel data of the first part may be obtained basedon the PET image data of the first part. A relationship between the CTimage data and PET voxel data of the second part may be determined. CTimage data of a third part of the subject may be determined based on therelationship and PET voxel data of the third part. The first part mayinclude the third part. An attenuation map may be determined based onthe CT image data of the second part and the third part. The PET imagedata of the first part may be corrected based on the attenuation map.

Yet another aspect of the present disclosure is directed to anon-transitory computer readable medium embodying a computer programproduct. The computer program product may include instructionsconfigured to cause a computing device to perform one or more of thefollowing operations. PET image data of a first part of a subject may beobtained. CT image data of a second part of the subject may be obtained.The first part may include the second part. PET voxel data of the firstpart may be obtained based on the PET image data of the first part. Arelationship between the CT image data and PET voxel data of the secondpart may be determined. CT image data of a third part of the subject maybe determined based on the relationship and PET voxel data of the thirdpart. The first part may include the third part. An attenuation map maybe determined based on the CT image data of the second part and thethird part. The PET image data of the first part may be corrected basedon the attenuation map.

In some embodiments, the first part may be a whole body of a subject,and the second part may be thorax, abdomen, upper limb, or lower limb ofthe subject.

In some embodiments, the obtaining of PET voxel data of the first partbased on the PET image data of the first part may include one or more ofthe following operations. A PET image may be reconstructed based on thePET image data of the first part, wherein the PET image of the firstpart may include PET voxel data of the first part.

In some embodiments, the relationship between the CT image data and PETvoxel data of the second part may be stored in a storage unit.

In some embodiments, the determination of CT image data of a third partbased on the relationship and PET voxel data of the third part mayinclude one or more of the following operations. The PET image of thethird part may be segmented into an osseous tissue region and anon-osseous tissue region. PET voxel data of the osseous tissue regionmay be obtained. CT image data of the osseous tissue region may bedetermined based on the relationship and the PET voxel data of theosseous tissue region.

In some embodiments, the determination of the CT image data of theosseous tissue region may include determining the CT image data of theosseous tissue region by interpolation or fitting.

In some embodiments, the determination of an attenuation map based onthe CT image data of the second part and the third part may include oneor more of the following operations. A first set of attenuationcoefficients may be determined based on the CT image data of the osseoustissue region of the third part. A second set of attenuationcoefficients of the non-osseous tissue region of the third part may bedetermined. A third set of attenuation coefficients may be determinedbased on the CT image data of the second part. An attenuation map of thefirst part may be determined based on the first set of attenuationcoefficients, the second set of attenuation coefficients, and the thirdset of attenuation coefficients.

In some embodiments, the first set of attenuation coefficients may bedetermined further based on a first linear conversion relation.

In some embodiments, the second set of attenuation coefficients may bedetermined based on a second linear conversion relation, or the secondset of attenuation coefficients may be set as a value equal to theattenuation coefficient of water.

In some embodiments, the method may further include one or more of thefollowing operations. A corrected PET image may be generated based onthe corrected PET image data.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device on which theprocessing device may be implemented according to some embodiments ofthe present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device on which the terminalmay be implemented according to some embodiments of the presentdisclosure;

FIG. 4 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for correctingPET image data of a first part of a subject according to someembodiments of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for determiningCT image data of a third part of the subject according to someembodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for determiningan attenuation map according to some embodiments of the presentdisclosure;

FIG. 8 is a flowchart illustrating an exemplary process for generating acorrected PET image according to some embodiments of the presentdisclosure;

FIG. 9 is a schematic diagram illustrating an exemplary CT image of anupper part of a body and an exemplary PET image of a whole bodyaccording to some embodiments of the present disclosure;

FIG. 10 is a schematic diagram illustrating exemplary CT images of anupper part of a body, knee joints, and ankle joints, and an exemplaryPET image of a whole body according to some embodiments of the presentdisclosure; and

FIG. 11 is a schematic diagram illustrating exemplary conversionrelations between CT values and attenuation coefficients of y raysaccording to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of example in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by otherexpression if they may achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or other storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in a firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to” or “coupled to” another unit,engine, module, or block, it may be directly on, connected or coupledto, or communicate with the other unit, engine, module, or block, or anintervening unit, engine, module, or block may be present, unless thecontext clearly indicates otherwise. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The terminology used herein is for the purposes of describing particularexamples and embodiments only, and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include,”and/or “comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof. It willbe further understood that the terms “construction” and“reconstruction,” when used in this disclosure, may represent a similarprocess in which an image may be transformed from data. Moreover, thephrase “image processing” and the phrase “image generation” may be usedinterchangeably. In some embodiments, image processing may include imagegeneration.

The present disclosure provided herein relates to an image processingsystem and method. Specifically, the method may be related to parametercorrection during an imaging process. The method and system may be usedin image reconstruction based on various image data acquired by ways of,for example, a positron emission tomography (PET) system, a computedtomography (CT) system, or the like, or a combination thereof.Specifically, the method and system may be used in a PET/CT imagingdevice. In some embodiments, the image generated by the PET/CT imagingdevice may include a 2D image, a 3D image, a 4D image, and/or anyrelated image data (e.g., projection data). It should be noted that theabove description of the image processing system and method is merelyprovided for the purposes of illustration, and not intended to limit thescope of the present disclosure. For persons having ordinary skills inthe art, multiple variations, changes, and/or modifications may be madeunder the guidance of the present disclosure. However, those variations,changes, and/or modifications do not depart from the scope of thepresent disclosure.

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure. As shown, theimaging system 100 may include a scanner 110, a processing device 120, astorage device 130, one or more terminals 140, and a network 150. Thecomponents in the imaging system 100 may be connected in one or more ofvarious ways. Merely by way of example, as illustrated in FIG. 1, thescanner 110 may be connected to the processing device 120 through thenetwork 150. As another example, the scanner 110 may be connected to theprocessing device 120 directly as indicated by the bi-directional arrowin dotted lines linking the scanner 110 and the processing device 120.As a further example, the storage device 130 may be connected to theprocessing device 120 directly or through the network 150. As still afurther example, one or more terminals 140 may be connected to theprocessing device 120 directly (as indicated by the bi-directional arrowin dotted lines linking the terminal 140 and the processing device 120)or through the network 150.

The scanner 110 may generate or provide image data via scanning asubject or a part of the subject. In some embodiments, the scanner 110may be a medical imaging device, for example, a PET device, a SPECTdevice, a CT device, or the like, or any combination thereof (e.g., aPET-CT device). In some embodiments, the scanner 110 may include asingle-modality scanner. The single-modality scanner may include, forexample, a computed tomography (CT) scanner, and/or a positron emissiontomography (PET) scanner. In some embodiments, the scanner 110 mayinclude both the CT scanner and the PET scanner. In some embodiments,image data of different modalities related to the subject, such as CTimage data and PET image data, may be acquired using different scannersseparately. In some embodiments, the scanner 110 may include amulti-modality scanner. In some embodiments, the multi-modality scannermay include a positron emission tomography-computed tomography (PET-CT)scanner. The multi-modality scanner may perform multi-modality imagingsimultaneously. For example, the PET-CT scanner may generate structuralX-ray CT image data and functional PET image data simultaneously in asingle scan.

In some embodiments, the subject may include a body, a substance, or thelike, or any combination thereof. In some embodiments, the subject mayinclude a specific portion of a body, such as a head, a thorax, anabdomen, an upper limb, a lower limb, or the like, or any combinationthereof. In some embodiments, the subject may include a specific organ,such as an esophagus, a trachea, a bronchus, a stomach, a gallbladder, asmall intestine, a colon, a bladder, a ureter, a uterus, a fallopiantube, a knee joint, an ankle joint, a thigh bone, a shin bone, etc. Insome embodiments, the subject may include a physical model (alsoreferred to as a mockup). The physical model may include one or morematerials constructed as different shapes and/or dimensions. Differentparts of the physical model may be made of different materials.Different materials may have different X-ray attenuation coefficients,different tracer isotopes, and/or different hydrogen proton contents.Therefore, different parts of the physical model may be recognized bythe imaging system 100. In the present disclosure, “object” and“subject” are used interchangeably. In some embodiments, the scanner 110may include a scanning table. The subject may be placed on the scanningtable for imaging.

In some embodiments, the scanner 110 may transmit the image data via thenetwork 150 to the processing device 120, the storage device 130, and/orthe terminal(s) 140. For example, the image data may be sent to theprocessing device 120 for further processing, or may be stored in thestorage device 130.

The processing device 120 may process data and/or information obtainedfrom the scanner 110, the storage device 130, and/or the terminal(s)140. For example, the processing device 120 may correct PET image databased on an attenuation map. In some embodiments, the processing device120 may be a single server or a server group. The server group may becentralized or distributed. In some embodiments, the processing device120 may be local or remote. For example, the processing device 120 mayaccess information and/or data from the scanner 110, the storage device130, and/or the terminal(s) 140 via the network 150. As another example,the processing device 120 may be directly connected to the scanner 110,the terminal(s) 140, and/or the storage device 130 to access informationand/or data. In some embodiments, the processing device 120 may beimplemented on a cloud platform. For example, the cloud platform mayinclude a private cloud, a public cloud, a hybrid cloud, a communitycloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like,or a combination thereof. In some embodiments, the processing device 120may be implemented by a computing device 200 having one or morecomponents as described in connection with FIG. 2.

The storage device 130 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 130 may store dataobtained from the scanner 110, the processing device 120, and/or theterminal(s) 140. In some embodiments, the storage device 130 may storedata and/or instructions that the processing device 120 may execute oruse to perform exemplary methods described in the present disclosure. Insome embodiments, the storage device 130 may include a mass storage, aremovable storage, a volatile read-and-write memory, a read-only memory(ROM), or the like, or any combination thereof. Exemplary mass storagemay include a magnetic disk, an optical disk, a solid-state drive, etc.Exemplary removable storage may include a flash drive, a floppy disk, anoptical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memory may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (EPROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM,etc. In some embodiments, the storage device 130 may be implemented on acloud platform as described elsewhere in the disclosure. Merely by wayof example, the cloud platform may include a private cloud, a publiccloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or any combination thereof.

In some embodiments, the storage device 130 may be connected to thenetwork 150 to communicate with one or more other components in theimaging system 100 (e.g., the processing device 120, the terminal(s)140, etc.). One or more components in the imaging system 100 may accessthe data or instructions stored in the storage device 130 via thenetwork 150. In some embodiments, the storage device 130 may be part ofthe processing device 120.

The terminal(s) 140 may be connected to and/or communicate with thescanner 110, the processing device 120, and/or the storage device 130.For example, the terminal(s) 140 may obtain a processed image from theprocessing device 120. As another example, the terminal(s) 140 mayobtain image data acquired by the scanner 110 and transmit the imagedata to the processing device 120 to be processed. In some embodiments,the terminal(s) 140 may include a mobile device 140-1, a tablet computer140-2, a laptop computer 140-3, or the like, or any combination thereof.For example, the mobile device 140-1 may include a mobile phone, apersonal digital assistance (PDA), a gaming device, a navigation device,a point of sale (POS) device, a laptop, a tablet computer, a desktop, orthe like, or any combination thereof. In some embodiments, theterminal(s) 140 may include an input device, an output device, etc. Theinput device may include alphanumeric and other keys that may be inputvia a keyboard, a touch screen (for example, with haptics or tactilefeedback), a speech input, an eye tracking input, a brain monitoringsystem, or any other comparable input mechanism. The input informationreceived through the input device may be transmitted to the processingdevice 120 via, for example, a bus, for further processing. Other typesof the input device may include a cursor control device, such as amouse, a trackball, or cursor direction keys, etc. The output device mayinclude a display, a speaker, a printer, or the like, or a combinationthereof. In some embodiments, the terminal(s) 140 may be part of theprocessing device 120.

The network 150 may include any suitable network that can facilitateexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the imaging system 100 (e.g., thescanner 110, the processing device 120, the storage device 130, theterminal(s) 140, etc.) may communicate information and/or data with oneor more other components of the imaging system 100 via the network 150.For example, the processing device 120 may obtain image data from thescanner 110 via the network 150. As another example, the processingdevice 120 may obtain user instruction(s) from the terminal(s) 140 viathe network 150. The network 150 may be and/or include a public network(e.g., the Internet), a private network (e.g., a local area network(LAN), a wide area network (WAN)), etc.), a wired network (e.g., anEthernet network), a wireless network (e.g., an 802.11 network, a Wi-Finetwork, etc.), a cellular network (e.g., a Long Term Evolution (LTE)network), a frame relay network, a virtual private network (VPN), asatellite network, a telephone network, routers, hubs, witches, servercomputers, and/or any combination thereof. For example, the network 150may include a cable network, a wireline network, a fiber-optic network,a telecommunications network, an intranet, a wireless local area network(WLAN), a metropolitan area network (MAN), a public telephone switchednetwork (PSTN), a Bluetooth™ network, a ZigBee™ network, a near fieldcommunication (NFC) network, or the like, or any combination thereof. Insome embodiments, the network 150 may include one or more network accesspoints. For example, the network 150 may include wired and/or wirelessnetwork access points such as base stations and/or internet exchangepoints through which one or more components of the imaging system 100may be connected to the network 150 to exchange data and/or information.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, thestorage device 130 may be a data storage including cloud computingplatforms, such as, public cloud, private cloud, community, and hybridclouds, etc. However, those variations and modifications do not departfrom the scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device on which theprocessing device may be implemented according to some embodiments ofthe present disclosure. As illustrated in FIG. 2, the computing device200 may include a processor 210, a storage 220, an input/output (I/O)230, and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code)and perform functions of the processing device 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, data structures,procedures, modules, and functions, which perform particular functionsdescribed herein. For example, the processor 210 may process image dataobtained from the scanner 110, the terminal(s) 140, the storage device130, and/or any other component of the Imaging system 100. In someembodiments, the processor 210 may include one or more hardwareprocessors, such as a microcontroller, a microprocessor, a reducedinstruction set computer (RISC), an application specific integratedcircuits (ASICs), an application-specific instruction-set processor(ASIP), a central processing unit (CPU), a graphics processing unit(GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both process A and process B, it should be understood thatprocess A and process B may also be performed by two or more differentprocessors jointly or separately in the computing device 200 (e.g., afirst processor executes process A and a second processor executesprocess B, or the first and second processors jointly execute processesA and B).

The storage 220 may store data/information obtained from the scanner110, the terminal(s) 140, the storage device 130, and/or any othercomponent of the Imaging system 100. In some embodiments, the storage220 may include a mass storage, a removable storage, a volatileread-and-write memory, a read-only memory (ROM), or the like, or anycombination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drives, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (EPROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage 220 may store a program for the processing device120 for determining one or more registration parameters related tomulti-modality images acquired by the imaging system 100.

The I/O 230 may input and/or output signals, data, information, etc. Insome embodiments, the I/O 230 may enable a user interaction with theprocessing device 120. In some embodiments, the I/O 230 may include aninput device and an output device. Examples of the input device mayinclude a keyboard, a mouse, a touch screen, a microphone, or the like,or a combination thereof. Examples of the output device may include adisplay device, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Examples of the display device may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), a touch screen, or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 150) to facilitate data communications. The communication port240 may establish connections between the processing device 120 and thescanner 110, the terminal(s) 140, and/or the storage device 130. Theconnection may be a wired connection, a wireless connection, any othercommunication connection that can enable data transmission and/orreception, and/or any combination of these connections. The wiredconnection may include, for example, an electrical cable, an opticalcable, a telephone wire, or the like, or any combination thereof. Thewireless connection may include, for example, a Bluetooth™ link, aWi-Fi™ link, a WiMax™ link, a WLAN link, a ZigBee link, a mobile networklink (e.g., 3G, 4G, 5G, etc.), or the like, or any combination thereof.In some embodiments, the communication port 240 may be and/or include astandardized communication port, such as RS232, RS485, etc. In someembodiments, the communication port 240 may be a specially designedcommunication port. For example, the communication port 240 may bedesigned in accordance with the digital imaging and communications inmedicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device on which the terminalmay be implemented according to some embodiments of the presentdisclosure. As illustrated in FIG. 3, the mobile device 300 may includea communication platform 310, a display 320, a graphic processing unit(GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory360, and a storage 390. In some embodiments, any other suitablecomponent, including but not limited to a system bus or a controller(not shown), may also be included in the mobile device 300. In someembodiments, a mobile operating system 370 (e.g., iOS™, Android™,Windows Phone™, etc.) and one or more applications 380 may be loadedinto the memory 360 from the storage 390 in order to be executed by theCPU 340. The applications 380 may include a browser or any othersuitable mobile apps for receiving and rendering information respect toimage processing or other information from the processing device 120.User interactions with the information stream may be achieved via theI/O 350 and provided to the processing device 120 and/or othercomponents of the imaging system 100 via the network 150.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or any other type of work station or externaldevice. A computer may also act as a server if appropriately programmed.

FIG. 4 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure. The processingdevice 120 may be implemented on the computing device 200 (e.g., theprocessor 210) illustrated in FIG. 2 or the mobile device 300 (e.g., theCPU 340) illustrated in FIG. 3. The processing device 120 may include anacquisition module 410, a reconstruction module 420, a mapping module430, a processing module 440, an attenuation coefficient determinationmodule 450, and a correction module 460.

The acquisition module 410 may be configured to acquire data.Specifically, the acquisition module 410 may acquire data via thecommunication port 240 or the I/O 230 shown in FIG. 2, or thecommunication platform 310 or the I/O 350 shown in FIG. 3.

In some embodiments, the acquisition module 410 may acquire data fromthe scanner 110 or the storage device 130, or data delivered by a userthrough the terminal 140. For example, the acquisition module 410 mayreceive PET image data and/or CT image data for reconstructing medicalimages from the scanner 110. As another example, the acquisition module410 may receive reconstructed medical images to be processed from thestorage device 130 or from the terminal 140. As a further example, theacquisition module 410 may receive control instructions inputted by auser through the terminal 140 or stored in the storage device 130. Insome embodiments, the acquisition module 410 may output data to othercomponents or parts in the processing device 120 or to the terminal 140.For example, images reconstructed by the processing device 120 may betransmitted to the storage device 130 from the acquisition module 410.As another example, intermediate data during processing such asattenuation coefficients may be delivered to the storage 120 from theacquisition module 410. In some embodiments, the CT image data receivedby the acquisition module 410 may be transmitted to the mapping module430 via the communication port 240 or the I/O 230 shown in FIG. 2, orthe communication platform 310 or the I/O 350 shown in FIG. 3.

The reconstruction module 420 may reconstruct one or more images basedon image data. For example, the reconstruction module 420 mayreconstruct a PET image based on PET image data. As another example, thereconstruction module 420 may reconstruct a CT image based on CT imagedata. In some embodiments, the reconstruction module 420 may transmitthe reconstructed PET image and/or CT image to the mapping module 430.In some embodiments, the reconstruction module 420 may reconstruct acorrected PET image based on corrected PET image data. Thereconstruction module 420 may reconstruct an image based on one or morereconstruction techniques described in the present disclosure. Moredescriptions of the reconstruction of the PET image and/or the CT imagemay be found elsewhere in the present disclosure (e.g., FIG. 5 and thedescription thereof).

The mapping module 430 may determine a relationship between CT imagedata and PET voxel data. In some embodiments, the mapping module 430 mayreceive the CT image data from the acquisition module 410. In someembodiments, mapping module 430 may receive the reconstructed PET imagefrom the reconstruction module 420 or the acquisition module 410. Forexample, the PET image may be reconstructed by the reconstruction module420 and then stored in the storage device 130. In some embodiments, themapping module 430 may extract the PET voxel data of the PET image anddetermine a relationship between the CT image data and the PET voxeldata. In some embodiments, the mapping module 430 may include a storageunit (not shown in FIG. 4), in which the relationship may be stored.More description of the relationship may be found elsewhere in thepresent disclosure (e.g., FIG. 5 and the description thereof).

In some embodiments, the processing module 440 may determine CT imagedata based on the relationship and PET voxel data. In some embodiments,the processing module 440 may obtain projection data by performingforward projection on an attenuation map. In some embodiments, theprocessing module 440 may receive PET voxel data and the relationshipfrom the mapping module 430, the reconstruction module 420, theacquisition module 410, and/or the storage device 130. In someembodiments, the processing module 440 may receive the attenuation mapfrom the acquisition module 410, the attenuation coefficientdetermination module 450, and/or the storage device 130.

The attenuation coefficient determination module 450 may determine oneor more attenuation coefficients. In some embodiments, the attenuationcoefficient determination module 450 may determine the attenuationcoefficients based on the CT image data and/or one or more conversionrelations. In some embodiments, the attenuation coefficientdetermination module 450 may determine a first sets of attenuationcoefficients based on a first conversion relation. In some embodiments,the attenuation coefficient determination module 450 may determine asecond sets of attenuation coefficients based on a second conversionrelation. In some embodiments, the attenuation coefficient determinationmodule 450 may determine a third sets of attenuation coefficients basedon the first conversion relation and/or the second conversion relation.In some embodiments, the attenuation coefficient determination module450 may determine an attenuation map based on the first, the second, andthe third sets of attenuation coefficients. In some embodiments, theattenuation coefficient determination module 450 may receive CT imagedata from the acquisition module 410, the reconstruction module 420,and/or the storage device 130.

The correction module 460 may receive PET image data and an attenuationmap. In some embodiments, the correction module 460 may receive the PETimage data from the scanner 110, the storage device 130, and/or thereconstruction module 420. In some embodiments, the correction module460 may correct the PET image data based on the attenuation map. In someembodiments, the correction module 460 may perform forward projection onthe attenuation map to obtain projection data. Then the correctionmodule 460 may correct the PET image data based on the projection data.In some embodiments, the correction module 460 may generate a correctedPET image based on the corrected PET image data. In some embodiments,the correction module 460 may correct the PET image data based on one ormore correction techniques described elsewhere in the presentdisclosure.

It should be noted that the above description of the processing device120 is provided for the purpose of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, modules may be combined in various ways, or connectedwith other modules as sub-systems. For example, in some embodiments, theprocessing module 440 may include a storage unit for storinginstruction(s) to be performed by the processing module 440. As anotherexample, the acquisition module 410 may include a wireless or wiredcommunication unit such as a transceiver for data transmission.

FIG. 5 is a flowchart illustrating an exemplary process for correctingPET image data of a first part of a subject according to someembodiments of the present disclosure. In some embodiments, one or moreoperations of process 500 illustrated in FIG. 5 for correcting PET imagedata of the first part of the subject may be implemented by theprocessing device 120. In some embodiments, one or more operations ofprocess 500 illustrated in FIG. 5 for correcting PET image data of thefirst part of the subject may be implemented in the imaging system 100illustrated in FIG. 1. For example, the process 500 illustrated in FIG.5 may be stored in the storage device 130 in the form of instructions,and invoked and/or executed by the processing device 120 (e.g., theprocessor 210 of the computing device 200 as illustrated in FIG. 2, theCPU 340 of the mobile device 300 as illustrated in FIG. 3). As anotherexample, a portion of the process 500 may be implemented on the scanner110. The operations of the illustrated process presented below areintended to be illustrative. In some embodiments, the process may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of the process as illustrated in FIG.5 and described below is not intended to be limiting.

In 510, PET image data of a first part may be obtained. Operation 510may be performed by the processing device 120 (e.g., the acquisitionmodule 410). In some embodiments, the PET image data of the first partmay be obtained from the scanner 110. In some embodiments, the PET imagedata generated by the scanner 110 may be stored temporally in thestorage device 130, and the processing device 120 may obtain the PETimage data from the storage device 130.

In 520, CT image data of a second part may be obtained. Operation 520may be performed by the processing device 120 (e.g., the acquisitionmodule 410). In some embodiments, the CT image data of the second partmay be obtained from the scanner 110. In some embodiments, the CT imagedata generated by the scanner 110 may be stored temporally in thestorage device 130, and the processing device 120 may obtain the CTimage data from the storage device 130.

In some embodiments, the subject may be a patient. In some embodiments,the first part may be the whole body of the subject, or any portionthereof. In some embodiments, the whole body may include an upper partof the body, a lower part of the body, or the like, or any combinationthereof. The upper part of the body may refer to a portion of the wholebody between a cranial vault and a pubic symphysis. For example, theupper part of the body may include an abdomen, a thorax, a back, a head,upper limb(s), etc. The lower part of the body may refer to a portion ofthe whole body between the pubic symphysis and the toes. For example,the lower part of the body may include legs, feet, etc. In someembodiments, the second part may be a portion of the whole body of thesubject. For example, the second part may be a head, a thorax, anabdomen, an upper limb, a lower limb, one or more knee joints, one ormore ankle joints, or the like, or any combination thereof. In someembodiments, the first part may include the second part. For example,the first part may be the whole body, and the second part may includethe upper part of the body. As another example, the first part may bethe whole body, and the second part may include the upper part of thebody, the knee joint(s), and/or the ankle joint(s), or the like. As afurther example, the first part may be the whole body, and the secondpart may include the lower part of the body. As still a further example,the first part may be the whole body, and the second part may includethe lower part of the body, the thorax, and/or the abdomen, or the like.As still a further example, the first part may be the upper part of thebody, and the second part may be the upper limb, the thorax, theabdomen, the head, or the like. As still a further example, the firstpart may be the lower part of the body, and the second part may be thelower limb, the knee joint(s), the ankle joint(s), the feet, or thelike.

In 530, PET voxel data of the first part may be obtained based on thePET image data of the first part. Operation 530 may be performed by theprocessing device 120 (e.g., the reconstruction module 420). In someembodiments, the PET image data may be raw data that are generated by aPET scanner, and a PET image may be reconstructed based on the PET imagedata of the first part. In some embodiments, the reconstruction module420 may reconstruct the PET image according to a reconstructiontechnique, generate reports including one or more PET images and/orother related information, and/or perform any other function for imagereconstruction in accordance with various embodiments of the presentdisclosure. The reconstruction technique may include an iterativereconstruction algorithm (e.g., a maximum likelihood expectationmaximization (MLEM) algorithm, an ordered subset expectationmaximization (OSEM) algorithm, maximum-likelihood reconstruction ofattenuation and activity (MLAA) algorithm, maximum-likelihoodattenuation correction factor (MLACF) algorithm, maximum likelihoodtransmission reconstruction (MLTR) algorithm, a conjugate gradientalgorithm, a maximum-a-posteriori estimation algorithm), a filtered backprojection (FBP) algorithm, a 3D reconstruction algorithm, or the like,or any combination thereof. The reconstructed PET image may include aplurality of voxels or pixels. In some embodiments, each voxel or pixelmay correspond to a voxel datum. Therefore, the reconstructed PET imagemay correspond to a plurality of PET voxel data of the first part. Insome embodiments, the reconstructed PET image and/or the PET voxel dataof the first part may be stored in the storage device 130, or temporallystored in the processing device 120. In some embodiments, the PET imagedata may be PET voxel data, and the PET voxel data of the first part maybe directly obtained from the PET image data of the first part. That isto say, operation 530 may be omitted.

In some embodiments, each PET voxel datum may have a gray value. Thegray values in a PET image of a subject may represent a distribution ofthe radioactive tracer isotope in the subject (e.g., the first part). Insome embodiments, the distribution of the radioactive tracer isotope mayindicate the radioactivity of the radioactive tracer isotope. The unitof the radioactivity of the radioactive tracer isotope may be Becquerel(Bq). In some embodiments, there may be a relationship between themetabolic rate of the subject and the radioactivity of the radioactivetracer isotope in the subject. If the radioactivity of the radioactivetracer isotope in a region of the subject is relatively high, it mayindicate that the metabolic rate of the region of the subject may berelatively high. If the radioactivity of the radioactive tracer isotopein a region of the subject is relatively low, it may indicate that themetabolic rate of the region of the subject may be relatively low. Insome embodiments, a relatively high gray value of a PET voxel datum mayindicate a relatively high metabolic rate and a relatively highradioactivity of the radioactive tracer isotope. Accordingly, arelatively low gray value of a PET voxel datum may indicate a relativelylow metabolic rate and a relatively low radioactivity of the radioactivetracer isotope. Therefore, the gray values of the PET voxel data mayreflect the metabolic rates of different regions in the subject. Forexample, a tissue or a lesion that has a relatively high metabolic ratemay correspond to PET voxel data with relatively high gray values. Asanother example, a tissue or a lesion that has a relatively lowmetabolic rate may correspond to PET voxel data with relatively low grayvalues.

In 540, a relationship between the CT image data of the second part andPET voxel data of the second part may be obtained. Operation 540 may beperformed by the processing device 120 (e.g., the mapping module 430).In some embodiments, the CT image data of the second part may be rawdata that are generated by a CT scanner, and a CT image may bereconstructed based on the CT image data of the second part. In someembodiments, the reconstruction module 420 may reconstruct the CT imageaccording to a reconstruction technique described elsewhere in thepresent disclosure. The reconstructed CT image may include a pluralityof voxels or pixels. In some embodiments, each voxel or pixel maycorrespond to a voxel datum. Therefore, the reconstructed CT image maycorrespond to a plurality of CT voxel data of the second part.

In some embodiments, the CT image data may be CT voxel data. In someembodiments, each CT voxel datum may have a CT value. The CT values ofthe CT image may represent quantified tissue densities in the subject(e.g., the second part). In some embodiments, the CT values may begenerated based on X-ray attenuation coefficients detected by a CTscanner. The unit of a CT value may be Hounsfield (Hu). In someembodiments, the CT values may be expressed by gray values in the CTimage. That is, the gray values may reflect the tissue densities in thesubject. For example, a relatively high gray value may indicate arelatively high tissue density, while a relatively low gray value mayindicate a relatively low tissue density. It should be noted that insome embodiments, the color of a CT image may be reversed. After colorreversing, a relatively low gray value may indicate a relatively hightissue density, while a relatively high gray value may indicate arelatively low tissue density.

Merely by way of example, in some embodiments, the CT value of water maybe 0 Hu, the CT value of osseous tissue may be larger than 1000 Hu, andthe CT value of air may be −1000 Hu. Accordingly, the CT value of softtissue may be within the range of 20-50 Hu, and the CT value of adiposetissue may be within the range of −40-90 Hu. Therefore, the tissue ofthe subject may be generally classified as water, osseous tissue, air,soft tissue, and adipose tissue.

In some embodiments, the mapping module 430 may determine therelationship between the CT image data and the PET voxel data of thesecond part. In some embodiments, the first part may include the secondpart, and the PET voxel data of the second part may be directly obtainedfrom the PET voxel data of the first part. In some embodiments, a CTimage datum may correspond to a PET voxel datum, and there may be acorrespondence between a CT value and a PET gray value. In someembodiments, an average value may be determined for one or more same orsimilar CT values or PET gray values to determine the relationship. Insome embodiments, the relationship may be expressed by one or morefunctions. The function(s) may include a polynomial function, atrigonometric function, a proportional function, an inverse proportionalfunction, an exponential function, a logarithmic function, or the like,or any combination thereof.

In some embodiments, the relationship may be expressed in the form of amapping table. In some embodiments, the mapping module 430 may include astorage unit in which the mapping table may be stored. In someembodiments, the mapping table may be stored in the storage device 130,and the mapping module 430 may obtain the mapping table through theacquisition module 410 from the storage device 130.

In 550, CT image data of a third part may be determined based on therelationship and PET voxel data of the third part. Operation 550 may beperformed by the processing device 120 (e.g., the processing module440). In some embodiments, the third part may be a portion of the firstpart. In some embodiments, the third part may be different from thesecond part. In some embodiments, the third part may be the rest part ofthe first part excluding the second part, or at least a portion of therest part. For example, if the first part is the whole body, and thesecond part is the upper part of the body, then the third part may bethe lower part of the body or a portion (e.g., an osseous tissue region)of the lower part of the body. As another example, if the first part isthe whole body, and the second part is the lower part of the body, thenthe third part may be the upper part of the body or a portion of theupper part of the body. In some embodiments, as the first part includesthe third part, the PET voxel data of the first part may be directlyobtained from the PET voxel data of the first part.

In some embodiments, a relationship between the CT image data of thethird part and the PET voxel data of the third part may be similar orthe same as the relationship between the CT image data of the secondpart and the PET voxel data of the third part. Therefore, the CT imagedata of the third part may be determined based on the relationshipdetermined in 540 and the PET voxel data of the third part. In someembodiments, the CT image data of the third part may be determined bylooking up a mapping table (e.g., the mapping table determined in 540)according to the PET voxel data of the third part. In some embodiments,a PET gray value of a pixel or voxel of the third part may be found onthe mapping table, and then a corresponding CT value of the same pixelor voxel may be read from the mapping table. In some embodiments, a PETgray value of a pixel or voxel of the third part may not be found on themapping table, and the corresponding CT value may not be directly readfrom the mapping table. In this case, the CT value may be determined byinterpolation or fitting. In some embodiments, the third part may besegmented into an osseous tissue region and a non-osseous tissue region.In some embodiments, the processing device 120 may determine CT imagedata of the osseous tissue region. More descriptions of thedetermination of the CT image data of the third part may be foundelsewhere in the present disclosure (e.g., FIG. 6 and the descriptionthereof).

For example, CT values of the voxels or pixels of the second part may be1000 Hu, 2000 Hu, . . . , and the corresponding PET gray values of thesecond part may be n₁, n₂, . . . . A relationship may be determinedbased on the CT values (i.e., 1000 Hu, 2000 Hu, . . . ) and the PET grayvalues (i.e., n₁, n₂, . . . ). If the PET gray values of the third part(e.g., the osseous tissue region) are m₁, m₂, . . . , then the CT valuesof the third part (e.g., the osseous tissue region) may be determined byinterpolation or fitting based on the relationship.

In 560, an attenuation map may be determined based on the CT image dataof the second part and the CT image data of the third part. Operation560 may be performed by the attenuation coefficient determination module450. The attenuation map may show a plurality of attenuationcoefficients of radiation rays (e.g., y rays) emitted from the scannedsubject. The attenuation map may include a plurality of attenuationcoefficients. In some embodiments, the attenuation map may betransmitted to the correction module 460 for further processing. Moredescriptions of the determination of the attenuation map may be foundelsewhere in the present disclosure (e.g., FIG. 7 and the descriptionthereof).

In 570, the PET image data of the first part may be corrected based onthe attenuation map. Operation 570 may be performed by the correctionmodule 460. In some embodiments, the corrected PET image data may bereconstructed to a corrected PET image. The corrected PET image may bedisplayed on a user interface, (e.g., the I/O 230) for diagnosis, or maybe stored in the storage device 130. More descriptions of the correctionof the PET image data may be found elsewhere in the present disclosure(e.g., FIG. 8 and the description thereof).

The CT image data of the third part can be determined based on the PETvoxel data of the third part and the relationship between the CT imagedata of the second part and the PET voxel data of the second part. Thatis, the CT image data of the third part can be obtained without scanningthe third part using a CT scanner. Therefore, the radiation doseintroduced to the subject can be reduced.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, theoperations 510 and 520 may be integrated into a single operation.

FIG. 6 is a flowchart illustrating an exemplary process for determiningCT image data of a third part of the subject according to someembodiments of the present disclosure. In some embodiments, one or moreoperations of process 600 illustrated in FIG. 6 for determining CT imagedata of the third part of the subject may be performed by the processingdevice 120 (e.g., the processing module 440). In some embodiments, oneor more operations of process 600 illustrated in FIG. 6 for determiningCT image data of the third part of the subject may be implemented in theimaging system 100 illustrated in FIG. 1. For example, the process 600illustrated in FIG. 6 may be stored in the storage device 130 in theform of instructions, and invoked and/or executed by the processingdevice 120 (e.g., the processor 210 of the computing device 200 asillustrated in FIG. 2, the CPU 340 of the mobile device 300 asillustrated in FIG. 3). As another example, a portion of the process 600may be implemented on the scanner 110. The operations of the illustratedprocess presented below are intended to be illustrative. In someembodiments, the process may be accomplished with one or more additionaloperations not described, and/or without one or more of the operationsdiscussed. Additionally, the order in which the operations of theprocess as illustrated in FIG. 6 and described below is not intended tobe limiting. In some embodiments, operation 550 illustrated in FIG. 5may be performed according to the process 600.

In 610, the PET image of the third part may be segmented into an osseoustissue region and a non-osseous tissue region. The osseous tissue regionmay include one or more voxels or pixels corresponding to one or morebones in the third part. The non-osseous tissue region may include oneor more voxels or pixels corresponding to tissues excluding bones (e.g.,soft tissue, water, adipose tissue, air, etc.). In some embodiments, theosseous tissue region may have a relatively low metabolic rate than thenon-osseous tissue region. In some embodiments, the osseous tissueregion may be a region of interest for attenuation correction of the PETimage. Therefore, it would be desirable to segment the osseous tissueregion from the PET image of the third part.

In some embodiments, the PET image of the third part may be segmentedbased on one or more segmentation algorithms. In some embodiments, thesegmentation algorithms may include a threshold segmentation algorithm,a region growing segmentation algorithm, an energy-based 3Dreconstruction segmentation algorithm, a level set-based segmentationalgorithm, a region split and/or merge segmentation algorithm, an edgetracking segmentation algorithm, a statistical pattern recognitionalgorithm, a C-means clustering segmentation algorithm, a deformablemodel segmentation algorithm, a graph search segmentation algorithm, aneural network segmentation algorithm, a geodesic minimal pathsegmentation algorithm, a target tracking segmentation algorithm, anatlas-based segmentation algorithm, a rule-based segmentation algorithm,a coupled surface segmentation algorithm, a model-based segmentationalgorithm, a deformable organism segmentation algorithm, a modelmatching algorithm, an artificial intelligence algorithm, or the like,or any combination thereof. For example, the segmentation algorithm maybe a region growing algorithm. In region growing, one or more voxels orpixels may be set as a seed for growing the osseous tissue. Then one ormore neighboring voxels or pixels of the seed with gray value(s) thatsatisfy a pre-determined condition may be incorporated with the seed.The pre-determined condition may relate to a gray value threshold, agray value difference between the neighboring voxels (or pixels) and theseed, or the like, or any combination thereof. In some embodiments, theneighboring voxels or pixels that satisfy the pre-determined conditionmay have the same or similar gray values. The region growing operationmay be repeated until all the voxels or pixels with the same or similargray values are incorporated with the seed, and accordingly, the osseoustissue region may be obtained. As the osseous tissue region is obtained,the non-osseous tissue region may be directly obtained from the PETimage of the third part. In some embodiments, the segmentationalgorithm(s) may be stored in the storage device 130, the storage 220,the storage 390, or another mobile storage device (e.g., a mobile harddisk, a USB flash disk, or the like, or a combination thereof). In someembodiments, the segmentation algorithm(s) may be retrieved from one ormore other external sources via the network 150.

In 620, PET voxel data of the osseous tissue region may be obtained. Insome embodiments, as the osseous tissue region is segmented, the voxelsor voxels belonging to the osseous tissue region may be determined, andaccordingly, the PET voxel data of the osseous tissue region may beobtained. In some embodiments, PET voxel data of the non-osseous tissueregion may be obtained.

In 630, CT image data of the osseous tissue region may be determinedbased on the relationship and the PET voxel data of the osseous tissueregion. In some embodiments, a PET gray value of a pixel or voxel of theosseous tissue region may be found on a mapping table (e.g., the mappingtable determined in 540), and then a corresponding CT value of the samepixel or voxel may be read from the mapping table. In some embodiments,a PET gray value of a pixel or voxel of the osseous tissue region maynot be found on the mapping table, and the corresponding CT value maynot be directly read from the mapping table. In this case, the CT valuemay be determined by interpolation or fitting. In some embodiments, CTimage data of the non-osseous tissue region may be determined based onthe relationship and the PET voxel data of the non-osseous tissueregion.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, the CTimage data of the non-osseous tissue region may be determined in asimilar way, and a CT image of the third part may be reconstructed basedon the CT image data of the osseous tissue region and the CT image dataof the non-osseous tissue region.

FIG. 7 is a flowchart illustrating an exemplary process for determiningan attenuation map according to some embodiments of the presentdisclosure. In some embodiments, one or more operations of process 700illustrated in FIG. 7 for determining the attenuation map may beperformed by the processing device 120 (e.g., the attenuationcoefficient determination module 450). In some embodiments, one or moreoperations of process 700 illustrated in FIG. 7 for determining theattenuation map may be implemented in the imaging system 100 illustratedin FIG. 1. For example, the process 700 illustrated in FIG. 7 may bestored in the storage device 130 in the form of instructions, andinvoked and/or executed by the processing device 120 (e.g., theprocessor 210 of the computing device 200 as illustrated in FIG. 2, theCPU 340 of the mobile device 300 as illustrated in FIG. 3). As anotherexample, a portion of the process 700 may be implemented on the scanner110. The operations of the illustrated process presented below areintended to be illustrative. In some embodiments, the process may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of the process as illustrated in FIG.7 and described below is not intended to be limiting. In someembodiments, operation 560 illustrated in FIG. 5 may be performedaccording to the process 700.

In 710, a first set of attenuation coefficients of the osseous tissueregion of the third part may be determined based on the CT image data(e.g., the CT image data of the osseous tissue region of the thirdpart). In some embodiments, each CT voxel or pixel of the osseous tissueregion of the third part may correspond to an attenuation coefficient ofthe first set of attenuation coefficients. The first set of attenuationcoefficients may refer to the attenuation coefficients of y rays emittedfrom the subject. In some embodiments, the first set of attenuationcoefficients may be further determined based on a first conversionrelation. The first conversion relation may indicate a conversionrelation between a plurality of attenuation coefficients and a pluralityof CT values within in a first range. In some embodiments, the firstconversion relation may be a traditional linear conversion relation thatis used in PET image correction based on CT image data. Moredescriptions of the first conversion relation may be found elsewhere inthe present disclosure (e.g., FIG. 11 and the description thereof).

It should be noted that tissues in different parts of a subject may havea certain consistency. Therefore, the relationship between CT image dataand PET voxel data in the second part may also be suitable for the thirdpart. Thus, the CT image data of the third part may be determined basedon the PET voxel data in the third part and the relationship.Accordingly, the CT values may be determined based on the CT image dataof the third part. Further, the first set of attenuation coefficientsmay be determined based on the first conversion relation and the CTvalues. In some embodiments, the first set of attenuation coefficientsmay be directly set according to a user input or a default setting ofthe imaging system 100. In some embodiments, the first set ofattenuation coefficients determined based on the relationship between CTimage data and PET voxel data in the second part may have relativelyhigh accuracy and/or relatively high reliability.

In 720, a second set of attenuation coefficients of the non-osseoustissue region of the third part may be determined. In some embodiments,each CT voxel or pixel of the non-osseous tissue region of the thirdpart may correspond to an attenuation coefficient of the second set ofattenuation coefficients. In some embodiments, as the non-osseous tissueregion generally includes soft tissue, adipose tissue, air, and/orwater, the attenuation coefficients of soft tissue and adipose tissueare similar as that of water, and the level of air in the third part isrelatively low, the second set of attenuation coefficients may be set asa value that equals to the attenuation coefficient of water.

In some embodiments, the second set of attenuation coefficients may bedetermined based on a second conversion relation and the CT image dataof the non-osseous tissue region. The second conversion relation mayindicate a conversion relation between a plurality of attenuationcoefficients and a plurality of CT values within in a second range. Insome embodiments, the second conversion relation may be a traditionallinear conversion relation that is used in PET image correction based onCT image data. More descriptions of the second conversion relation maybe found elsewhere in the present disclosure (e.g., FIG. 11 and thedescription thereof).

In 730, a third set of attenuation coefficients may be determined basedon the CT image data of the second part. In some embodiments, each CTvoxel or pixel of the second part may correspond to an attenuationcoefficient of the third set of attenuation coefficients. In someembodiments, the third set of attenuation coefficients may be determinedaccording to the first conversion relation and/or the second conversionrelation as described in FIG .11.

In 740, an attenuation map of the first part may be determined based onthe first, the second, and the third sets of attenuation coefficients.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example,operation 720 and/or operation 730 may be performed before operation710.

FIG. 8 is a flowchart illustrating an exemplary process for generating acorrected PET image according to some embodiments of the presentdisclosure. In some embodiments, one or more operations of process 800illustrated in FIG. 8 for generating the corrected PET image may beperformed by the processing device 120 (e.g., the correction module460). In some embodiments, one or more operations of process 800illustrated in FIG. 8 for generating the corrected PET image may beimplemented in the imaging system 100 illustrated in FIG. 1. Forexample, the process 800 illustrated in FIG. 8 may be stored in thestorage device 130 in the form of instructions, and invoked and/orexecuted by the processing device 120 (e.g., the processor 210 of thecomputing device 200 as illustrated in FIG. 2, the CPU 340 of the mobiledevice 300 as illustrated in FIG. 3). As another example, a portion ofthe process 800 may be implemented on the scanner 110. The operations ofthe illustrated process presented below are intended to be illustrative.In some embodiments, the process may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process as illustrated in FIG. 8 and described below is not intendedto be limiting. In some embodiments, operation 570 illustrated in FIG. 5may be performed according to the process 800.

In 810, projection data may be obtained by performing forward projectionon the attenuation map.

In 820, the PET image data of the first part may be corrected based onthe projection data. In some embodiments, the PET image data may beobtained from the acquisition module 410, the reconstruction module 420,the mapping module 430, and/or the processing module 440. In someembodiments, processing device 120 (e.g., the correction module 460) maycorrect the PET image data based on one or more correction techniques.The correction technique may include a random correction, a scattercorrection, an attenuation correction, a dead time correction,normalization, or the like, or any combination thereof.

In 830, a corrected PET image may be generated based on the correctedPET image data. In some embodiments, the corrected PET image may begenerated based on one or more reconstruction techniques describedelsewhere in the present disclosure.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, thegeneration of the corrected PET image may be performed by thereconstruction module 420. As another example, the forward projectionmay be performed by the processing module 440.

FIG. 9 is a schematic diagram illustrating an exemplary CT image of anupper part of a body and an exemplary PET image of a whole bodyaccording to some embodiments of the present disclosure. As shown inFIG. 9, the PET image 20 of the whole body 30 includes a PET image 21 ofthe upper part of the body 31 and a PET image 22 of the lower part ofthe body 32. The CT image 10 of the upper part of the body 31 is alsoshown in FIG. 9. The PET images 20 was reconstructed based on the PETimage data of the whole body. The CT image 10 was reconstructed based onthe CT image data of the upper part of the body. A relationship betweenthe CT image data in the CT image 10 of the upper part of the body 31and the PET voxel data in the PET image 21 of the upper part of the body31 was obtained. Then CT image data of the lower part of the body 32 maybe determined based on PET voxel data in the PET image 22 of the lowerpart of the body 32 and the relationship. An osseous tissue regionand/or a non-osseous tissue region may be segmented from the lower partof the body 32. A first set of attenuation coefficients of the osseoustissue region of the lower part of the body 32 may be obtained based onthe CT image data of the osseous tissue region of the lower part of thebody 32. A second set of attenuation coefficients of the non-osseoustissue region of the lower part of the body 32 may be obtained based onthe CT image data of the non-osseous tissue region of the lower part ofthe body 32. A third sets of attenuation coefficients may be determinedbased on the CT image data in the CT image 10 of the upper part of thebody 31. Then an attenuation map may be obtained based on the first, thesecond, and the third sets of attenuation coefficients. PET image dataof the whole body 30 may be corrected based on the attenuation map. Acorrected PET image may be generated based on the corrected PET imagedata.

In some embodiments, one or more portions in joint regions (e.g.,ligament, meniscus, etc.) may have similar metabolic rate as osseoustissue region, and then the gray values of these portions may be similaras those of the osseous tissue region. Therefore, these portions mayintroduce an error in the segmentation of the osseous tissue region ofthe lower part of the body 32, and accordingly, the accuracy of thefirst sets of attenuation coefficients may be affected. In someembodiments, it would be desirable to scan these portions and obtain CTimage data of these portions to improve the accuracy of the first setsof attenuation coefficients. FIG. 10 is a schematic diagram illustratingexemplary CT images of an upper part of a body, knee joints, and anklejoints, and an exemplary PET image of a whole body according to someembodiments of the present disclosure. As shown in FIG. 10, the PETimage 20 of the whole body may include a PET image 21 of the upper partof the body and a PET image 22 of the lower part of the body. The CTimages may include a CT image 10 of the upper part of the body 31, a CTimage of the knee joints 33 and a CT image of ankle joints 34. In thiscase, the first part may be the whole body, the second part may includethe upper part of the body 31, the knee joints 33, and the ankle joints34, and the third part may be the region of the lower part of the bodyexcluding the knee joints 33 and the ankle joints 34.

It should be noted that the above description of the processing moduleis merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. For example, the second part may include one or more otherorgans in the body. As another example, the three CT images of the upperpart of the body 31, the knee joint 33, and the ankle joints 34 may bedetected under different radiation doses of γ rays.

FIG. 11 is a schematic diagram illustrating exemplary conversionrelations between CT values and attenuation coefficients of γ raysaccording to some embodiments of the present disclosure. In someembodiments, the first, the second, and/or the third sets of attenuationcoefficients may be determined based on the curve.

As shown in FIG. 11, the conversion relations between the attenuationcoefficients and the CT values may be linear. The horizontal axisindicates CT values, and the vertical axis indicates attenuationcoefficients. The attenuation coefficients of γ rays may be determinedrelative to the electron density of water, and may have no unit. In someembodiments, the first conversion relation may be expressed as Equation(1):

$\begin{matrix}{\mu_{1} = \left\{ {\begin{matrix}{{{a_{1}({CT})} + b_{1}},} & k_{1} \\{{{a_{2}({CT})} + b_{2}},} & k_{2} \\{\ldots \mspace{14mu} \ldots \mspace{14mu} \ldots \mspace{14mu} \ldots} & \; \\{{{a_{N}({CT})} + b_{N}},} & k_{N}\end{matrix},} \right.} & (1)\end{matrix}$

where μ₁ may be the first sets of attenuation coefficients of theosseous tissue region of the third part, k₁, k₂, . . . , kN may bedifferent radiation intensities of the X-rays for obtaining the CT imagedata of the second part, a₁, a₂, . . . , aN and b₁, b₂, . . . , b_(N)may be constants greater than zero under different radiationintensities, N may be a natural number greater than zero, and the symbol“CT” may represent the CT values with unit Hu. In some embodiments,Equation (1) may be simplified as μ₁=a_(n,k) _(n) (CT)+b_(n), whereinn=1, 2, . . . N.

For example, if the radiation intensity is 80 kVp (see the dash linelabelled as “a” in FIG. 11), the conversion relation may be expressed asμ_(80kVp)=0.0004(CT)+1, in which the CT value may be no less than 0 Hu.As another example, if the radiation intensity is 140 kVp (see the dashline labelled as “b” in FIG. 11), the conversion relation may beexpressed as μ_(140kVp)=0.000625(CT)+1, in which the CT value may be noless than 0 Hu.

As shown in FIG. 11, there is a solid line labelled as “c” near the dashline labelled as “a” and a solid line labelled as “d” near the dash linelabelled as “b”. The dash line “a” and the dash line “b” areexperimental relations between the CT values and the attenuationcoefficients under 80 kVp and 140 kVp respectively, while the solid line“c” and the solid line “d” are clinical relations between the CT valuesand the attenuation coefficients under 80 kVp and 140 kVp respectively.There may be an error between the experimental relations and theclinical relations. However, the error is within an acceptable andtolerable range.

In some embodiments, the second conversion relation of the non-osseoustissue region may be expressed as Equation (2):

μ₂ =a′(CT)+b′,   (2)

where μ₂ may be the second set of attenuation coefficients of thenon-osseous tissue region of the third part, a′, b′ may be constantsgreater than zero, and the CT value may be no less than 0 Hu. Merely byway of example, the second conversion relation may be expressed asμ₂=0.001(CT)+1.

In some embodiments, the second set of attenuation coefficients may bedirectly determined as the value that equals to the attenuationcoefficient of water.

As shown in FIG. 11, the line(s) indicating the second conversionrelation of the non-osseous tissue region and the line indicating thefirst conversion relation of the osseous tissue region may share acommon end point, which may benefit the segmentation of non-osseoustissue region and osseous tissue region in the corrected PET image.

It shall be noticed that many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments.

In some embodiments, a tangible and non-transitory machine-readablemedium or media having instructions recorded thereon for a processor orcomputer to operate an imaging system to perform one or more functionsof the modules or units described elsewhere herein.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “block,” “module,” “engine,” “unit,” “component,” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing processing device or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

1-20. (canceled)
 21. A method implemented on a computing machine havingat least one processor and at least one storage device, the methodcomprising: obtaining a PET image generated based on PET image data of afirst part of a subject, the PET image including PET voxel data, and thefirst part of the subject including a second part and a third part;obtaining a CT image of the second part generated based on CT image dataof the second part, the CT image of the second part including multipleCT voxels; determining a relationship between the CT image of the secondpart and PET voxel data of the second part; segmenting PET image of thethird part into an osseous tissue region and a non-osseous tissueregion; obtaining PET voxel data of the osseous tissue region of thethird part and PET voxel data of the non-osseous tissue region of thethird part; determining CT image data of the osseous tissue region basedon the relationship and the PET voxel data of the osseous tissue region;determining CT image data of the non-osseous tissue region based on therelationship and the PET voxel data of the non-osseous tissue region,determining a first set of attenuation coefficients based on the CTimage data of the osseous tissue region of the third part and a firstlinear conversion relation, the first linear conversion relationindicating a conversion relation between a plurality of attenuationcoefficients and a plurality of CT values within in a first range thatcorresponds to an osseous tissue; determining a second set ofattenuation coefficients of the non-osseous tissue region of the thirdpart based on the CT image data of the non-osseous tissue region of thethird part and a second linear conversion relation, the second linearconversion relation indicating a conversion relation between a pluralityof attenuation coefficients and a plurality of CT values within in asecond range that corresponds to a non-osseous tissue; determining athird set of attenuation coefficients based on the CT image data of thesecond part; and determining an attenuation map based on the first setof attenuation coefficients, the second set of attenuation coefficients,and the third set of attenuation coefficients.
 22. The method accordingto claim 21, wherein the first part is a whole body of the subject, andthe second part is at least one part of thorax, abdomen, upper limb, orlower limb of the subject.
 23. The method according to claim 21, whereinthe relationship between the CT image of the second part and the PETvoxel data of the second part indicates correspondence between a firstaverage value of values of one or more CT voxels and a second averagevalue of values of one or more PET voxels in an osseous tissue region ora non-osseous tissue region of the second part.
 24. The method accordingto claim 21, wherein the relationship between the CT image of the secondpart and the PET voxel data of the second part is expressed in a form ofa mapping table or by one or more functions including at least one of apolynomial function, a trigonometric function, a proportional function,an inverse proportional function, an exponential function, or alogarithmic function.
 25. The method according to claim 21, wherein therelationship between the CT image of the second part and the PET voxeldata of the second part is stored in a storage unit.
 26. The methodaccording to claim 21, wherein the CT image data of the osseous tissueregion is determined by interpolation or fitting.
 27. The methodaccording to claim 21, wherein the first range is no less than 0 Hu, andthe second range is less than 0 Hu.
 28. The method according to claim21, wherein the first linear conversion relation relates to radiationintensities of X-rays for obtaining the CT image data of the secondpart, and the second linear conversion relation is irrelevant toradiation intensities of the X-rays for obtaining the CT image data ofthe second part.
 29. The method of claim 21, further comprising:correcting the PET image data based on the attenuation map; andgenerating a corrected image based on the corrected PET image data. 30.A system comprising: at least one storage device including a set ofinstructions or programs; and at least one processor configured tocommunicate with the at least one storage device, wherein when executingthe set of instructions or programs, the at least one processor isconfigured to cause the system to perform operations including:obtaining a PET image generated based on PET image data of a first partof a subject, the PET image including PET voxel data, and the first partof the subject including a second part and a third part; obtaining a CTimage of the second part generated based on CT image data of the secondpart, the CT image of the second part including multiple CT voxels;determining a relationship between the CT image of the second part andPET voxel data of the second part; segmenting PET image of the thirdpart into an osseous tissue region and a non-osseous tissue region;obtaining PET voxel data of the osseous tissue region of the third partand PET voxel data of the non-osseous tissue region of the third part;determining CT image data of the osseous tissue region based on therelationship and the PET voxel data of the osseous tissue region;determining CT image data of the non-osseous tissue region based on therelationship and the PET voxel data of the non-osseous tissue region,determining a first set of attenuation coefficients based on the CTimage data of the osseous tissue region of the third part and a firstlinear conversion relation, the first linear conversion relationindicating a conversion relation between a plurality of attenuationcoefficients and a plurality of CT values within in a first range thatcorresponds to an osseous tissue; determining a second set ofattenuation coefficients of the non-osseous tissue region of the thirdpart based on the CT image data of the non-osseous tissue region of thethird part and a second linear conversion relation, the second linearconversion relation indicating a conversion relation between a pluralityof attenuation coefficients and a plurality of CT values within in asecond range that corresponds to a non-osseous tissue; determining athird set of attenuation coefficients based on the CT image data of thesecond part; and determining an attenuation map based on the first setof attenuation coefficients, the second set of attenuation coefficients,and the third set of attenuation coefficients.
 31. The system accordingto claim 30, wherein the first part is a whole body of the subject, andthe second part is at least one part of thorax, abdomen, upper limb, orlower limb of the subject.
 32. The system according to claim 30, whereinthe relationship between the CT image of the second part and the PETvoxel data of the second part indicates correspondence between a firstaverage value of values of one or more CT voxels and a second averagevalue of values of one or more PET voxels in an osseous tissue region ora non-osseous tissue region of the second part.
 33. The system accordingto claim 30, wherein the relationship between the CT image of the secondpart and the PET voxel data of the second part is expressed in a form ofa mapping table or by one or more functions including at least one of apolynomial function, a trigonometric function, a proportional function,an inverse proportional function, an exponential function, or alogarithmic function.
 34. The system according to claim 30, wherein therelationship between the CT image of the second part and the PET voxeldata of the second part is stored in a storage unit.
 35. The systemaccording to claim 30, wherein the CT image data of the osseous tissueregion is determined by interpolation or fitting.
 36. The systemaccording to claim 30, wherein the first range is no less than 0 Hu, andthe second range is less than 0 Hu.
 37. The system according to claim30, wherein the first linear conversion relation relates to radiationintensities of X-rays for obtaining the CT image data of the secondpart, and the second linear conversion relation is irrelevant toradiation intensities of the X-rays for obtaining the CT image data ofthe second part.
 38. The system of claim 30, further comprising:correcting the PET image data based on the attenuation map; andgenerating a corrected image based on the corrected PET image data. 39.A non-transitory computer readable medium embodying a computer programproduct, the computer program product comprising instructions configuredto cause a computing device to perform a method, the method comprising:obtaining a PET image generated based on PET image data of a first partof a subject, the PET image including PET voxel data, and the first partof the subject including a second part and a third part; obtaining a CTimage of the second part generated based on CT image data of the secondpart, the CT image of the second part including multiple CT voxels;determining a relationship between the CT image of the second part andPET voxel data of the second part; segmenting PET image of the thirdpart into an osseous tissue region and a non-osseous tissue region;obtaining PET voxel data of the osseous tissue region of the third partand PET voxel data of the non-osseous tissue region of the third part;determining CT image data of the osseous tissue region based on therelationship and the PET voxel data of the osseous tissue region;determining CT image data of the non-osseous tissue region based on therelationship and the PET voxel data of the non-osseous tissue region,determining a first set of attenuation coefficients based on the CTimage data of the osseous tissue region of the third part and a firstlinear conversion relation, the first linear conversion relationindicating a conversion relation between a plurality of attenuationcoefficients and a plurality of CT values within in a first range thatcorresponds to an osseous tissue; determining a second set ofattenuation coefficients of the non-osseous tissue region of the thirdpart based on the CT image data of the non-osseous tissue region of thethird part and a second linear conversion relation, the second linearconversion relation indicating a conversion relation between a pluralityof attenuation coefficients and a plurality of CT values within in asecond range that corresponds to a non-osseous tissue; determining athird set of attenuation coefficients based on the CT image data of thesecond part; and determining an attenuation map based on the first setof attenuation coefficients, the second set of attenuation coefficients,and the third set of attenuation coefficients.
 40. The non-transitorycomputer readable medium of claim 39, wherein the method furthercomprises: correcting the PET image data based on the attenuation map;and generating a corrected image based on the corrected PET image data.